How a CDP Unlocks the Power of AI for Marketers

How a CDP Unlocks the Power of AI for Marketers

Data scientists, BI professionals, and marketers have learned to expect a status quo when it comes to using artificial intelligence and marketing: it is time-consuming and requires handoffs between team members, marketing systems for data collection, and activation. According to HBR, “It has been a common trope that 80% of a data scientist’s valuable time is spent simply finding, cleaning, and organizing data, leaving only 20% to actually perform analysis.” When it comes to building models on customer data for marketing, data scientists are handcuffed by fragmented marketing systems that don’t provide a single source of truth and aren’t able to act on the output of models due to system disparity.

Over the past year, we’ve worked with over a dozen customers to find the right solution to these problems- and that’s why we’re excited to officially launch BlueConic’s AI Workbench. With direct access to a unified profile database and connections to marketing activation channels, AI Workbench enables data science and marketing teams to work more efficiently. Data scientists and business intelligence (BI) teams have the ability to import notebooks and customize models within a customer data platform. Marketers will have built-in models that they can tweak and test, without having to hard code them or rely solely on data science teams to pull customer scores when they need them.

Shared efficiencies across and between data science, BI, and marketing teams

What’s this look like in action? Let’s take a look at a few examples:

Access to unified customer data for modeling.

As mentioned above, data scientists spend 80% cleaning data, pulling lists, deduping data, merging contacts and only 20% of their doing higher level work that they were hired to do: helping the business solve a challenge and make decisions that impact the bottom line. Through AI Workbench, data scientists can pull data from a single, unified, customer database. Being built on a profile database, customer data is persistent, and profiles update in real-time at an individual level. Data scientists can import their own models or tune pre-existing models in BlueConic by selecting the specific profile properties (i.e. customer attributes) they want to use or pull in data via an API. For example, a data scientist could hypothesize which customer attributes influence a customer’s risk for churn; then pull those attributes into a model. Relying on a CDP as a data source allows them to look at a broader range of attributes that update in real-time which makes the model smarter.

Getting models into production with ease.

After a data scientist builds a model for a business user (i.e. marketer), it is often difficult to get the model into production across various external systems. AI workbench makes it easy to apply a model against certain profile properties and store the result as a part of the profiles. Data scientists benefit from this in two ways:

They can quickly experiment with different model approaches (e.g. if the click-through rate of an email based on a predicted next best action is not actually better than the control group, the data scientist can easily change the model and try again)

Sending the result/prediction/customer score to multiple platforms is just as easy as sending it to a single platform. For example, you could use a model to calculate customer lifetime value (CLV), create segments based on high or low CLV scores, then send these segments back out to your email service provider, CRM system, and to personalize on-site experiences all in one place.

AI Workbench brings your models closer to the data and reduces data latency. Models are pulling from profile data that is constantly being updated, then profiles are updated with data from notebooks as often you’ve scheduled. Let’s say you’ve created an uplift model to understand which customers are likely to purchase regardless of whether or not you spend ad dollars on them; and which customer will not buy regardless of the number of ad dollars you spend on them. You can save on your ad spend and focus specifically on those that need to be persuaded – driving up your click-through rate, return on ad spend, and lower cost per acquisition.

Shared efficiencies across and between teams.

The proximity of models to customer data and marketing systems allows marketing and data science teams to limit the number of hand-offs between teams and iterate on ideas at an accelerated rate. For example, with parameters, data scientists can flag different variables in code to serve as the “plug-and-play” variable. A marketer can go in and select one of the available variables to then create models on their own.